24 datasets found

The dataset used in the paper for text-to-3D content creation, which consists of 3D assets generated from text prompts.

Text-to-3D generation has made remarkable progress recently, particularly with methods based on Score Distillation Sampling (SDS) that leverages pre-trained 2D diffusion models.

A novel pose-Controlled text-to-3D Animal Generation framework which generates a high quality 3D animal consistent with a given pose.

Debiased Score Distillation Sampling (D-SDS) is a method for improving the realism and alleviating the Janus problem of generated 3D objects.

CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout

The dataset used in the paper for text-to-3D generation, generated using pre-trained text-to-image diffusion models.

Text2Shape is a dataset of 8,447 table instances and 6,591 chair instances from the ShapeNet dataset, along with 75,344 natural language descriptions.

Text-to-3D generation has shown promising results, yet common challenges such as the Multi-face Janus problem and extended generation time for high-quality assets. In this...

The dataset used in the Sculpt3D paper, containing 100 text prompts for 3D object generation.

Magic3D: High-resolution text-to-3D content creation.

Prolific-Dreamer: High-fidelity and diverse text-to-3D generation with variational score distillation.

The dataset used in the paper for text-to-3D generation, consisting of 5,000 different prompts and 800,000 generated images.

The dataset used in the paper for testing the HeadSculpt model, which consists of 3D head avatars generated from textual prompts.

The dataset used in the paper for training and testing the HeadSculpt model, which consists of 3D head avatars generated from textual prompts.

The dataset used in the paper is a text-to-3D generation framework, named the LucidDreamer, to distill high-fidelity textures and shapes from pretrained 2D diffusion models.

Text-to-3D generation has recently garnered significant attention, fueled by 2D diffusion models trained on billions of image-text pairs. Existing methods primarily rely on...

Text-to-3D generation using 2D visual prompts

The dataset used in the paper SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity

Text-to-3D generation has emerged as an important application that enables non-experts to easily create 3D contents. The conventional approaches for text-to-3D train a...

CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting